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Visual fixations and the computation and comparison of value in simple choice

Nature Neuroscience volume 13, pages 1292–1298 (2010)Cite this article

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An Erratum to this article was published on 26 August 2011

This article has been updated

Abstract

Most organisms facing a choice between multiple stimuli will look repeatedly at them, presumably implementing a comparison process between the items' values. Little is known about the nature of the comparison process in value-based decision-making or about the role of visual fixations in this process. We created a computational model of value-based binary choice in which fixations guide the comparison process and tested it on humans using eye-tracking. We found that the model can quantitatively explain complex relationships between fixation patterns and choices, as well as several fixation-driven decision biases.

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Figure 1: Experiment and model.
Figure 2: Basic psychometrics.
Figure 3: Fixation properties.
Figure 4: Basic model predictions.
Figure 5: Choice biases.
Figure 6: Alternative models.

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Change history

  • 10 February 2011

    In the version of this article initially published, there were symbols dropped from the equations in the second paragraph of the results section. The term θright should have been θrright in the first equation and the term θleft should have been θrleft in the second equation. The error has been corrected in the HTML and PDF versions of the article.

References

  1. Rangel, A., Camerer, C. & Montague, P.R. A framework for studying the neurobiology of value-based decision making. Nat. Rev. Neurosci. 9, 545–556 (2008).

    Article  CAS  Google Scholar 

  2. Wallis, J.D. Orbitofrontal cortex and its contribution to decision-making. Annu. Rev. Neurosci. 30, 31–56 (2007).

    Article  CAS  Google Scholar 

  3. Padoa-Schioppa, C. & Assad, J.A. Neurons in the orbitofrontal cortex encode economic value. Nature 441, 223–226 (2006).

    Article  CAS  Google Scholar 

  4. Kahneman, D. & Tversky, A. Prospect theory: an analysis of decision under risk. Econometrica 4, 263–291 (1979).

    Article  Google Scholar 

  5. Mas-Colell, A., Whinston, M. & Green, J. Microeconomic Theory (Oxford University Press, New York, 1995).

  6. Luce, R.D. Response Times: Their Role in Inferring Elementary Mental Organization (Oxford University Press, Oxford, 1986).

  7. Stone, M. Models for choice-reaction time. Psychometrika 25, 251–260 (1960).

    Article  Google Scholar 

  8. Ratcliff, R. A theory of memory retrieval. Psychol. Rev. 85, 59–108 (1978).

    Article  Google Scholar 

  9. Ratcliff, R., Cherian, A. & Segraves, M. A comparison of macaque behavior and superior colliculus neuronal activity to predictions from models of two-choice decisions. J. Neurophysiol. 90, 1392–1407 (2003).

    Article  Google Scholar 

  10. Ratcliff, R. & Smith, P. A comparison of sequential sampling models for two-choice reaction time. Psychol. Rev. 111, 333–367 (2004).

    Article  Google Scholar 

  11. Laming, D. A critical comparison of two random-walk models for choice reaction time. Acta Psychol. (Amst.) 43, 431–453 (1979).

    Article  Google Scholar 

  12. Link, S.W. The Wave Theory of Difference and Similarity (Lawrence Erlbaum, Hillsdale, New Jersey, 1992).

  13. Usher, M. & McClelland, J. The time course of perceptual choice: the leaky, competing accumulator model. Psychol. Rev. 108, 550–592 (2001).

    Article  CAS  Google Scholar 

  14. Smith, P. Psychophysically-principled models of visual simple reaction time. Psychol. Rev. 102, 567–593 (1995).

    Article  Google Scholar 

  15. Smith, P. Stochastic dynamic models of response time and accuracy: a foundational primer. J. Math. Psychol. 44, 408–463 (2000).

    Article  CAS  Google Scholar 

  16. Ditterich, J. Stochastic models of decisions about motion direction: behavior and physiology. Neural Netw. 19, 981–1012 (2006).

    Article  Google Scholar 

  17. Bogacz, R. Optimal decision-making theories: linking neurobiology with behaviour. Trends Cogn. Sci. 11, 118–125 (2007).

    Article  Google Scholar 

  18. Gold, J.I. & Shadlen, M.N. Neural computations that underlie decisions about sensory stimuli. Trends Cogn. Sci. 5, 10–16 (2001).

    Article  Google Scholar 

  19. Gold, J.I. & Shadlen, M.N. Banburisms and the brain: decoding the relationship between sensory stimuli, decisions and reward. Neuron 36, 299–308 (2002).

    Article  CAS  Google Scholar 

  20. Churchland, A.K., Kiani, R. & Shadlen, M.N. Decision-making with multiple alternatives. Nat. Neurosci. 11, 693–702 (2008).

    Article  CAS  Google Scholar 

  21. Ditterich, J. Stochastic models of decisions about motion direction: behavior and physiology. Neural Netw. 19, 981–1012 (2006).

    Article  Google Scholar 

  22. Ditterich, J., Mazurek, M.E. & Shadlen, M.N. Microstimulation of visual cortex affects the speed of perceptual decisions. Nat. Neurosci. 6, 891–898 (2003).

    Article  CAS  Google Scholar 

  23. Gold, J.I. & Shadlen, M.N. Banburisms and the brain: decoding the relationship between sensory stimuli, decisions and reward. Neuron 36, 299–308 (2002).

    Article  CAS  Google Scholar 

  24. Gold, J.I. & Shadlen, M.N. The neural basis of decision making. Annu. Rev. Neurosci. 30, 535–574 (2007).

    Article  CAS  Google Scholar 

  25. Hanks, T.D., Ditterich, J. & Shadlen, M.N. Microstimulation of macaque area LIP affects decision-making in a motion discrimination task. Nat. Neurosci. 9, 682–689 (2006).

    Article  CAS  Google Scholar 

  26. Huk, A.C. & Shadlen, M.N. Neural activity in macaque parietal cortex reflects temporal integration of visual motion signals during perceptual decision making. J. Neurosci. 25, 10420–10436 (2005).

    Article  CAS  Google Scholar 

  27. Mazurek, M.E., Roitman, J.D., Ditterich, J. & Shadlen, M.N. A role for neural integrators in perceptual decision making. Cereb. Cortex 13, 1257–1269 (2003).

    Article  Google Scholar 

  28. Palmer, J., Huk, A.C. & Shadlen, M.N. The effect of stimulus strength on the speed and accuracy of a perceptual decision. J. Vis. 5, 376–404 (2005).

    Article  Google Scholar 

  29. Roitman, J.D. & Shadlen, M.N. Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. J. Neurosci. 22, 9475–9489 (2002).

    Article  CAS  Google Scholar 

  30. Shadlen, M.N. & Newsome, W.T. Motion perception: seeing and deciding. Proc. Natl. Acad. Sci. USA 93, 628–633 (1996).

    Article  CAS  Google Scholar 

  31. Shadlen, M.N. & Newsome, W.T. Neural basis of a perceptual decision in the parietal cortex (area LIP) of the rhesus monkey. J. Neurophysiol. 86, 1916–1936 (2001).

    Article  CAS  Google Scholar 

  32. Armel, K.C., Beaumel, A. & Rangel, A. Biasing simple choices by manipulating relative visual attention. Judgm. Decis. Mak. 3, 396–403 (2008).

    Google Scholar 

  33. Shimojo, S., Simion, C., Shimojo, E. & Sheier, C. Gaze bias both reflects and influences preference. Nat. Neurosci. 6, 1317–1322 (2003).

    Article  CAS  Google Scholar 

  34. Bogacz, R. Optimal decision-making theories: linking neurobiology with behaviour. Trends Cogn. Sci. 11, 118–125 (2007).

    Article  Google Scholar 

  35. Bogacz, R., Brown, E., Moehlis, J., Holmes, P. & Cohen, J.D. The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced choice tasks. Psychol. Rev. 113, 700–765 (2006).

    Article  Google Scholar 

  36. Reddi, B.A.J. & Carpenter, R.H.S. The influence of urgency on decision time. Nat. Neurosci. 3, 827–830 (2000).

    Article  CAS  Google Scholar 

  37. Roe, R.M., Busemeyer, J. & Townsend, J.T. Multialternative decision field theory: a dynamic connectionist model of decision making. Psychol. Rev. 108, 370–392 (2001).

    Article  CAS  Google Scholar 

  38. Busemeyer, J. & Townsend, J.T. Decision field theory: a dynamic-cognitive approach to decision-making in an uncertain environment. Psychol. Rev. 100, 432–459 (1993).

    Article  CAS  Google Scholar 

  39. Busemeyer, J. & Johnson, J.G. Computational models of decision making. in Blackwell Handbook of Judgment and Decision Making (eds. Koehler, D. & Narvey, N.) 133–154 (Blackwell, Oxford, 2004).

  40. McClelland, J. & Rumelhart, D. An interactive activation model of context effects in letter perception. I. An account of basic findings. Psychol. Rev. 88, 375–407 (1981).

    Article  Google Scholar 

  41. Plassmann, H., O'Doherty, J. & Rangel, A. Orbitofrontal cortex encodes willingness to pay in everyday economic transactions. J. Neurosci. 27, 9984–9988 (2007).

    Article  CAS  Google Scholar 

  42. Hare, T.A., O'Doherty, J., Camerer, C.F., Schultz, W. & Rangel, A. Dissociating the role of the orbitofrontal cortex and the striatum in the computation of goal values and prediction errors. J. Neurosci. 28, 5623–5630 (2008).

    Article  CAS  Google Scholar 

  43. Erk, S., Spitzer, M., Wunderlich, A., Galley, L. & Walter, H. Cultural objects modulate reward circuitry. Neuroreport 13, 2499–2503 (2002).

    Article  Google Scholar 

  44. Arana, F.S. et al. Dissociable contributions of the human amygdala and orbitofrontal cortex to incentive motivation and goal selection. J. Neurosci. 23, 9632–9638 (2003).

    Article  CAS  Google Scholar 

  45. Paulus, M.P. & Frank, L.R. Ventromedial prefrontal cortex activation is critical for preference judgments. Neuroreport 14, 1311–1315 (2003).

    Article  Google Scholar 

  46. Valentin, V.V., Dickinson, A. & O'Doherty, J. Determining the neural substrates of goal-directed learning in the human brain. J. Neurosci. 27, 4019–4026 (2007).

    Article  CAS  Google Scholar 

  47. Hare, T.A., Camerer, C.F. & Rangel, A. Self-control in decision-making involves modulation of the vmPFC valuation system. Science 324, 646–648 (2009).

    Article  CAS  Google Scholar 

  48. Itti, L. & Koch, C. Computational modelling of visual attention. Nat. Rev. Neurosci. 2, 194–203 (2001).

    Article  CAS  Google Scholar 

  49. Spezio, M.L., Adolphs, R., Hurley, R.S.E. & Piven, J. Abnormal use of facial information in high-functioning autism. J. Autism Dev. Disord. 37, 929–939 (2007).

    Article  Google Scholar 

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Acknowledgements

We thank E. Johnson, P. Bossaerts and C. Koch for comments and J. Pulst-Korenberg for help with data collection. This work received financial support from the Moore Foundation.

Author information

Authors and Affiliations

  1. Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, California, USA.,

    Ian Krajbich & Antonio Rangel

  2. Precourt Institute for Energy Efficiency, Stanford University, Palo Alto, California, USA

    Carrie Armel

  3. Computational and Neural Systems, California Institute of Technology, Pasadena, California, USA

    Antonio Rangel

Authors
  1. Ian Krajbich
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  2. Carrie Armel
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  3. Antonio Rangel
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Contributions

A.R. and C.A. devised the experiment. I.K. programmed and conducted the experiment, performed the analyses and co-wrote the manuscript. A.R. designed the model, co-wrote the manuscript and supervised the project.

Corresponding author

Correspondence to Antonio Rangel.

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The authors declare no competing financial interests.

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Supplementary Figures 1–30 and Supplementary Table 1 (PDF 7631 kb)

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Krajbich, I., Armel, C. & Rangel, A. Visual fixations and the computation and comparison of value in simple choice. Nat Neurosci 13, 1292–1298 (2010). https://doi.org/10.1038/nn.2635

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